Accurate recognition and tracking of human faces are indispensable in applications like Face Recognition, Forensics, etc. The need for enhancing the low resolution faces for such applications has gathered more attention in the past few years. To recognize the faces from the surveillance video footage, the images need to be in a significantly recognizable size. Image Super-Resolution (SR) algorithms aid in enlarging or super-resolving the captured low-resolution image into a high-resolution frame. It thereby improves the visual quality of the image for recognition. This paper discusses some of the recent methodologies in face super-resolution (FSR) along with an analysis of its performance on some benchmark databases. Learning based methods are by far the immensely used technique. Sparse representation techniques, Neighborhood-Embedding techniques, and Bayesian learning techniques are all different approaches to learning based methods. The review here demonstrates that, in general, learning based techniques provides better accuracy/ performance even though the computational requirements are high. It is observed that Neighbor Embedding provides better performances among the learning based techniques. The focus of future research on learning based techniques, such as Neighbor Embedding with Sparse representation techniques, may lead to approaches with reduced complexity and better performance.

Shadows are physical phenomena that appear on a surface when direct light from a source is unable to reach the surface due to the presence of an object between the source and the surface. The formation of shadows and their various features has evolved as a topic of discussion among researchers. Though the presence of shadows can aid us in understanding the scene model, it might impair the performance of applications such as object detection. Hence, the removal of shadows from videos and images is required for the faultless working of certain image processing tasks. This paper presents a survey of notable shadow removal techniques for single image available in the literature. For the purpose of the survey, the various shadow removal algorithms are classified under five categories, namely, reintegration methods, relighting methods, patch-based methods, color transfer methods, and interactive methods. Comparative study of qualitative and quantitative performances of these works is also included. The pros and cons of various approaches are highlighted. The survey concludes with the following observations- (i) shadow removal should be performed in real time since it is usually considered as a preprocessing task, (ii) the texture and color information of the regions underlying the shadow must be recovered, (iii) there should be no hard transition between shadow and non-shadow regions after removing the shadows.